DESIGN AND IMPLEMENTATION OF A GENE NETWORK REVERSE ENGINEERING METHOD BASED ON MUTUAL INFORMATION
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, the authors describe a gene network reverse engineering method, which employs mutual information to infer the connections between the genes. Given the expression profile of the genes, determined for different conditions, the method calculates the similarity matrix corresponding to the mutual information between each pair of genes. The approximated matrix can be subsequently refined by using a data processing inequality and an analytically estimated threshold for the statistical significance of mutual information. The authors have used the proposed method to reconstruct a network of 2041 gene transcription factors, measured over 79 human tissues. The numerical results show that the connectivity of the transcription factors network is characterized by a scale free distribution, with an exponent of the power law between 1.5 and 2. The power law for the connectivity distribution implies that the network is extremely heterogeneous; i.e., its topology is dominated by a few highly connected genes, which link the rest of the loosely-connected genes to the system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it